Partial multi-label learning (PML) aims to train a classifier on dataset whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when datasets are collected from crowd-sourcing platform. Existing works primarily approach it from a curriculum learning perspective, leveraging the memorization effect to disambiguate noisy labels and produce robust predictions. However, these methods are based on non-adaptive weighting functions and lack theoretical guidance for optimal weighting. To overcome these issues, a calibrated disambiguation model named PML-CD is proposed. We firstly formulate the optimal weighting function for curriculum-based disambiguation, which is equivalent to the calibration of the model's predicted confidences, thus provide a guidance for curriculum designing. To obtain the optimal weighting function from PML dataset during the training, a transferable calibrator is designed, which takes the histogram of positive samples' confidences as input, and outputs the optimal curriculum weighting for training. Prototype alignment regularization is also proposed to promote the model's performance. Experiments conducted on Pascal VOC, MS-COCO, NUS-WIDE and CUB have verified that our method outperforms existing state-of-the-art PML methods.

We present an automatic generation pipeline of interactive nonlinear video for online apparel shopping navigation. Our approach was inspired by Google's “Messy Middle” theory, which suggests that people mentally are faced with two tasks—exploration and evaluation—before purchasing online. Given a set of apparel product presentation videos, our navigation UI organizes them to optimize users' product exploration and automatically generates interactive videos for users' product evaluation. To support automatic methods, we proposed a video clustering similarity ($\operatorname{CSIM}$) and a camera movement similarity ($\operatorname{MSIM}$), as well as a comparative video generation algorithm for product recommendation, presentation, and comparison. To evaluate our pipeline's effectiveness, we conducted several user studies. The results showed that our pipeline can help users complete the consumption process more efficiently, making it easier for them to understand and choose a product.